The present invention generally relates to performing fault diagnosis of turbine engines and, more specifically, to an automated method for providing such fault diagnosis.
The gas turbine propulsion engine is one of the most critical subsystems within an aircraft. A great deal of research has been carried out in the field of gas turbine propulsion engine fault diagnosis and prognosis. The process of diagnosis requires adequate operating data input which is not always available or provided. The mid-sized jet propulsion engine, in particular, may typically be operated with less-expensive data acquisition systems, because of cost constraints incurred in design and operation, and the quality of operating data output suffers as a result. For example, engine output data may be recorded only during certain pre-determined operating intervals of time. Accordingly, only a limited number of parameters related to the core engine performance are routinely recorded and available for analysis. Moreover, conventional diagnostic methods typically fail to take into account operating differences resulting from engine-to-engine manufacturing variations.
Many of the heuristics derived from an expert's knowledge and understanding of engine performance are not systematic in nature. Accordingly, the current practice in the field has mostly relied on manual trending of the observations as performed by the experts. Although the present state of the art discloses unconventional methods for enhancing manual observation and control, such as the method incorporating a self-organizing map for controlling process events of a technical plant taught by U.S. Pat. No. 6,314,413 issued to Otte, such procedures are not generally applicable to the diagnosis and prognosis methods required in the maintenance and operation of the gas turbine engine. In addition, conventional methods of engine diagnosis may not provide a confidence level or belief factor for a determination that the engine condition is faulty or normal.
As can be seen, there is a need for an improved method of performing fault diagnosis and prognosis of turbine engines.